Intelligent electric energy meter capable of monitoring and reporting operation error

Abstract

The invention relates to the technical field of power state monitoring, in particular to an intelligent electric energy meter capable of monitoring and reporting operation errors, and the electric energy meter comprises an error operation parameter monitoring module, an error space-time correlation modeling module, an error propagation path analysis module, an error dynamic regulation and control module, an error adaptability verification module and an error transmission state summarization module. According to the method, the operation deviation is captured in real time, the abnormal time period and nodes are marked, the abnormal event positioning and recording capacity is improved, an abnormal distribution panorama is constructed, path optimization is supported, a high-error path is screened by analyzing a weight value and an influence factor, an error propagation hierarchical structure is optimized, and the load proportion and the connection path are dynamically adjusted; multiple paths are configured to reduce the transmission risk, the system robustness is enhanced, the state response and error correction precision is improved through real-time monitoring and abnormal verification, key information is archived, analyzed and extracted to generate a report record, and intelligence and high efficiency of electric energy meter error monitoring are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power status monitoring, and particularly to an intelligent electric energy meter capable of monitoring and reporting operation errors. Background Art

[0002] The technical field of power status monitoring mainly focuses on the comprehensive monitoring and management of the operation status of power systems, including the acquisition of operation parameters of power equipment, power quality analysis, fault early warning and diagnosis, and the transmission and processing of real-time data. The core of this field lies in the high-precision monitoring of key nodes in the power system through intelligent and automated technologies to ensure the stability and security of power supply, while improving operation efficiency. Typical application scenarios include power grid status monitoring, load analysis, and operation fault diagnosis of power equipment, which are widely used in modern power systems.

[0003] Among them, an intelligent electric energy meter capable of monitoring and reporting operation errors is an advanced device integrating metering, monitoring, and data reporting functions. Its main purpose is to accurately measure power consumption, while also monitoring the operation status of the device in real time. In particular, the automatic detection and reporting functions of errors can help users promptly understand the working conditions of the electric energy meter and improve the accuracy and reliability of power metering. This type of intelligent electric energy meter is widely used in industrial, commercial, and household electricity consumption scenarios and is an important part of modern power system management.

[0004] In the existing technology, in the abnormal analysis of data acquisition and monitoring nodes, there is a lack of in-depth characterization of abnormal parameter distributions and dynamic regulation means, resulting in the system being able to only achieve simple detection and early warning of abnormalities, and it is difficult to provide accurate abnormal time intervals and node association information. The monitoring of electric energy meters mainly focuses on the static recording of single parameters, ignoring the spatial distribution of abnormal propagation paths and the analysis of influencing factors, making it difficult to effectively identify and optimize high-proportion paths. The absence of real-time load adjustment and multi-path transfer mechanisms makes the existing solutions prone to system instability in the case of uneven load distribution or single-path faults. The existing technology has weak capabilities for dynamic monitoring and adaptive verification of abnormal paths, lacking archived analysis based on actual operation data and an efficient feedback mechanism. This limitation leads to key node problems not being captured and processed in a timely manner, thereby reducing the reliability and accuracy of electric energy meter monitoring. Summary of the Invention

[0005] To address the technical problems in the prior art that in data collection and abnormal analysis of monitoring nodes, there is a lack of in-depth characterization of abnormal parameter distributions and dynamic regulation means, resulting in the system being able to only achieve simple detection and warning of abnormalities, and it is difficult to provide accurate abnormal time intervals and node association information. The monitoring of electric energy meters mainly focuses on the static recording of single parameters, ignoring the spatial distribution of abnormal propagation paths and the analysis of influencing factors, making it difficult to effectively identify and optimize high-proportion paths. The absence of real-time load adjustment and multi-path transmission mechanisms makes the existing solutions prone to system instability in the case of uneven load distribution or single-path failures. The prior art has weak capabilities for dynamically monitoring and adaptively verifying abnormal paths, lacking an archiving analysis and efficient feedback mechanism based on actual operation data. This limitation leads to key node problems not being captured and processed in a timely manner, thereby reducing the reliability and accuracy of electric energy meter monitoring. Embodiments of the present invention provide an intelligent electric energy meter capable of monitoring and reporting operating errors. The technical solutions are as follows:

[0006] On the one hand, an intelligent electric energy meter capable of monitoring and reporting operating errors is provided. The electric energy meter includes:

[0007] The error operating parameter monitoring module periodically collects the current value, voltage value, and load power value of the electric energy meter, extracts abnormal states, performs time sorting and interval division, and establishes a record of parameter abnormal sequences;

[0008] The error spatio-temporal correlation modeling module extracts abnormal node distances and adjacency relationships based on the record of parameter abnormal sequences, performs spatial grouping annotation and time frequency amplitude analysis, and generates an error spatio-temporal distribution matrix;

[0009] The error propagation path analysis module extracts node weight values and influencing factors based on the error spatio-temporal distribution matrix, accumulates node propagation relationships, screens high-error paths, and constructs an error transfer path map;

[0010] The error dynamic regulation module adjusts the load ratio and connection paths based on the error transfer path map, screens standby nodes, allocates low-risk transfer paths, and generates a dynamic load allocation table;

[0011] The error adaptability verification module collects load data, monitors the current and voltage change ranges, verifies the path stability, records the response situation, and obtains the node adaptability verification result based on the dynamic load allocation table;

[0012] The error transfer status summary module collects error data and archives it, analyzes the node error change situation, extracts key data points, and generates a record table for monitoring and reporting operating errors based on the node adaptability verification result.

[0013] As a further solution of the present invention, the parameter anomaly sequence record includes an anomaly time period, an anomaly node marker, and an anomaly parameter value distribution. The error spatio-temporal distribution matrix includes node distance grouping, spatial correlation annotation, time series frequency analysis, and time series amplitude analysis. The error transmission path map includes node transmission weights, influence factor distribution, propagation path direction, and path hierarchy. The dynamic load distribution table includes critical node load ratios, standby node configurations, low-risk path allocations, and multi-path switching schemes. The node adaptability verification result includes critical node anomaly markers, path response statuses, current and voltage change range monitoring results, and path connection stability verification. The operation error monitoring and reporting record table includes error data archiving records, inter-node error change analysis, adaptability verification information extraction, and critical data point statistics.

[0014] As a further solution of the present invention, the error operation parameter monitoring module includes:

[0015] The data capture sub-module regularly reads current, voltage, and power data based on the working status of the electricity meter, performs data collection at set time intervals, records signal loss and fluctuations, saves the collected data after verification, and obtains the operation status data.

[0016] The anomaly detection sub-module compares the current, voltage, and power ranges based on the operation status data, filters out anomaly data, marks frequent and sudden fluctuations, classifies and eliminates invalid anomaly points, and obtains an anomaly data set.

[0017] The anomaly time series analysis sub-module sorts the anomaly data based on the time stamp, divides it into fixed time intervals, calculates the anomaly frequency in the intervals, marks the frequent anomaly time periods, extracts nodes and associates events, and establishes a parameter anomaly sequence record.

[0018] As a further solution of the present invention, the error spatio-temporal correlation modeling module includes:

[0019] The node spatial relationship analysis sub-module extracts node coordinate data based on the parameter anomaly sequence record, pairs and calculates the node distances, normalizes the distance values, marks the un-matched nodes as anomaly points, and generates a node distance matrix.

[0020] The spatial correlation grouping sub-module extracts adjacent relationship node pairs based on the node distance matrix, groups and annotates the nodes according to the distance threshold, maps the grouping information to the matrix, sorts and filters the anomaly node pairs, and generates a spatial correlation annotation result.

[0021] The cross-node analysis sub-module analyzes the time series change trend based on the spatial correlation annotation result, combines the grouped data to analyze the node time correlation, marks the anomaly correlation nodes, and generates an error spatio-temporal distribution matrix.

[0022] As a further solution of the present invention, the error propagation path analysis module includes:

[0023] The weight value extraction sub-module analyzes the node numerical relationship based on the error spatio-temporal distribution matrix, extracts the transfer path weight value, matches and adjusts the weight value data, summarizes the node association relationship, and obtains the path transfer weight data;

[0024] The path weight superposition sub-module extracts the path node sequence based on the path transfer weight data, accumulates the node weight values, screens the node combinations with high weights, sorts and screens the node paths, checks the integrity of the accumulation process, and generates a high-weight propagation path set;

[0025] The node propagation construction sub-module analyzes the node transfer sequence using the dynamic time warping algorithm based on the high-weight propagation path set, extracts the propagation direction, classifies and reorganizes the node transfer structure, arranges the continuous topological structure, and arranges the transfer path of the nodes into a continuous topological structure to generate an error transfer path map.

[0026] As a further solution of the present invention, the formula of the dynamic time warping algorithm is as follows:

[0027]

[0028] Among them, R ij represents the abnormal association score value between node i and node j, S i and S j respectively represent the spatial association intensity values of node i and node j, T ij represents the difference value of the time series of node i and node j, C i and C j respectively represent the classification feature values of node i and node j in the grouped data, α is the weight coefficient of spatial association, β is the adjustment coefficient of time series change, and γ is the weight coefficient of grouping characteristics.

[0029] As a further solution of the present invention, the error dynamic regulation module includes:

[0030] The load distribution adjustment sub-module extracts the key node load ratio data based on the error transfer path map, screens the nodes exceeding the threshold, redistributes the connection path load data, allocates and adjusts the load to adjacent nodes, and obtains the load adjustment distribution data;

[0031] The spare node screening sub-module analyzes the nodes with low load values based on the load adjustment distribution data, screens the nodes with remaining capacity as spare nodes, marks the paths as spare paths, updates the node and path association structure, and generates the spare node allocation result;

[0032] Based on the spare node allocation result, the path load configuration sub-module extracts the spare node transfer path information, reallocates the high-load path data, transfers the load to the spare path, updates the load value and verifies the structure, and generates a dynamic load allocation table.

[0033] As a further solution of the present invention, the error adaptability verification module includes:

[0034] Based on the dynamic load allocation table, the node load monitoring sub-module analyzes the node load data, tracks the load change range, marks the nodes beyond the range, records the corresponding relationship between the load value and the time point, and obtains the dynamic node load data;

[0035] Based on the dynamic node load data, the path stability verification sub-module analyzes the connection paths of the marked nodes, checks the path load recovery time and stability, filters the continuously changing paths, and obtains the path response status data;

[0036] Based on the path response status data, the node adaptability recording sub-module analyzes the key nodes of the abnormal paths, classifies and marks the overloaded nodes, calculates the adaptability adjustment load value of the nodes, records the node load and delay characteristics, optimizes the node hierarchy structure, and generates the node adaptability verification result.

[0037] As a further solution of the present invention, calculate the adaptability adjustment load value of the node according to the formula:

[0038]

[0039] where L adj represents the adaptability adjustment load value of the node, L i represents the current load value of the i-th node, μ represents the average value of the node load, D i represents the delay time of the i-th node, μ d represents the average value of the node delay, C node represents the number of connections of the node, W 1 、W 2 、W 3 are weight parameters.

[0040] As a further solution of the present invention, the error transfer status summary module includes:

[0041] Based on the node adaptability verification result, the error data archiving sub-module extracts the node error information, classifies and arranges the time point and the node number, eliminates the duplicate records, archives them in the order of time and nodes, and generates the node error archiving record;

[0042] The key error extraction sub-module extracts the error data between nodes based on the node error archive record, marks the data points beyond the range, sorts out the node numbers and time series distribution, sorts the error values and marks the associated paths, and generates a set of key error data points.

[0043] The error monitoring and summarization sub-module analyzes the distribution law and node paths based on the set of key error data points, records the cumulative situation of node and path errors, sorts out the association relationships into a structured table, integrates the node status and error information, and generates a running error monitoring and reporting record form.

[0044] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0045] By periodically collecting data such as current, voltage, and load power of the electric energy meter and extracting abnormal state data, it is possible to capture the running deviation in real time and mark the abnormal time periods and related nodes. This accurate data capture and time differentiation enhance the ability to locate and record abnormal events. Use the distance values and adjacency relationships between abnormal nodes for spatial association grouping, and combine the frequency and amplitude analysis of the time series to analyze the correlation of cross nodes, realizing a panoramic expression of the abnormal distribution, providing basic support for subsequent path optimization. By analyzing the weight values and influencing factors between nodes, accumulating in combination with the node propagation direction and hierarchical relationship, screening out the key paths with a relatively high error proportion, not only improving the visualization level of the error source, but also optimizing the hierarchical structure of error propagation, dynamically adjusting the load ratio data and connection paths, and reducing the risk of single-path transmission through multi-path configuration, ensuring the robustness of the system. The real-time monitoring of node loads and the verification of abnormal paths make the state response more timely and ensure the accuracy of error correction. Through the archiving and analysis of error data, extracting the abnormal information of key nodes and generating a reporting record, improving the transparency and analysis value of the electric energy meter monitoring data. This data-driven dynamic management and control method realizes the intelligentization, precision, and high efficiency of electric energy meter error monitoring. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0047] Figure 1 It is a schematic diagram of an intelligent electric energy meter with monitorable and reportable running errors provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of the electric energy meter framework of the present invention;

[0049] Figure 3 This is the flowchart of the error operation parameter monitoring module in the present invention;

[0050] Figure 4 This is the flowchart of the error spatio-temporal correlation modeling module in the present invention;

[0051] Figure 5 This is the flowchart of the error propagation path analysis module in the present invention;

[0052] Figure 6 This is the flowchart of the error dynamic regulation module in the present invention;

[0053] Figure 7 This is the flowchart of the error adaptability verification module in the present invention;

[0054] Figure 8 This is the flowchart of the error transfer status summary module in the present invention. Detailed implementation manners

[0055] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either of the two can be selected.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0058] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0059] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0060] The embodiments of the present invention provide an intelligent electric energy meter capable of monitoring and reporting operation errors, such as Figure 1-2Schematic diagram of an intelligent electric energy meter capable of monitoring and reporting operating errors. The system includes:

[0061] The error operation parameter monitoring module periodically collects the current value, voltage value, and load power value of the electric energy meter, extracts the operating state data exceeding the abnormal threshold, sorts and divides the time distribution, marks the frequently occurring abnormal time periods and associated nodes, and establishes a parameter abnormal sequence record;

[0062] The error spatio-temporal correlation modeling module extracts the distance values and adjacency relationships between abnormal nodes based on the parameter abnormal sequence record, groups and labels the spatial correlations between nodes, analyzes the cross-node correlation based on the frequency and amplitude of the time series, and generates an error spatio-temporal distribution matrix;

[0063] The error propagation path analysis module extracts the weight values and influence factors of the transfer relationships between nodes based on the error spatio-temporal distribution matrix, accumulates and superimposes the weight values of the propagation paths affecting the nodes, screens the paths with a high error ratio, draws the propagation directions and hierarchical relationships of the nodes, and constructs an error transfer path map;

[0064] The error dynamic regulation module adjusts the proportional data of the load distribution and the connection paths of the key nodes based on the error transfer path map, screens the standby nodes and reallocates the connection values, performs load configuration and switching operations, allocates multiple low-risk transfer paths, and generates a dynamic load allocation table;

[0065] The error adaptability verification module collects the node load data based on the dynamic load allocation table, dynamically monitors the change ranges of the current and voltage, verifies the stability of the path connections, extracts the key nodes with abnormal states and records the path response conditions, and obtains the node adaptability verification results;

[0066] The error transfer state summary module collects and archives the error data of the electric energy meter monitoring nodes based on the node adaptability verification results, analyzes the error change conditions between nodes, records and analyzes the error information generated during the node adaptability verification process, extracts the key data points in the error information, and generates an operating error monitoring and reporting record form.

[0067] The parameter anomaly sequence record includes the anomaly time period, anomaly node markers, and the distribution of anomaly parameter values. The error spatio-temporal distribution matrix includes node distance grouping, spatial correlation annotation, time series frequency analysis, and time series amplitude analysis. The error transmission path map includes node transmission weights, influence factor distribution, propagation path direction, and path hierarchy. The dynamic load distribution table includes the load ratios of key nodes, backup node configurations, low-risk path allocation, and multi-path switching schemes. The node adaptability verification results include key node anomaly markers, path response status, monitoring results of the current and voltage change ranges, and verification of path connection stability. The operation error monitoring and reporting record table includes error data archiving records, analysis of error changes between nodes, extraction of adaptability verification information, and statistics of key data points.

[0068] Specifically, as Figure 2 、 3 shown, the error operation parameter monitoring module includes:

[0069] The data capture sub-module regularly reads current, voltage, and power data based on the working status of the electricity meter, performs data acquisition at set time intervals, records signal loss and fluctuations, saves the acquired data after verification, and obtains the operation status data.

[0070] At each acquisition, the electricity meter reading instruction is initiated through the control logic, the sampling period and sampling window are set, the raw data acquired each time is written into the temporary storage area. For the acquired current and voltage data, the high-frequency components within the period are separated, and the low-frequency stable part is selected as the valid data. For the power data, the instantaneous power, average power, and fluctuation range are calculated, the change trend of the power factor is recorded. At the same time, the signal strength threshold is set, the signal loss events and their durations are recorded, and the signal fluctuation amplitudes are recorded in categories. The specific moments of signal loss and fluctuations are marked with time tags to ensure the integrity of signal recording. After each acquisition, the accuracy of the acquired data is checked through data verification rules. For example, the physical upper and lower limit ranges of the current and voltage are checked point by point, the point values that are significantly inconsistent with the actual situation are excluded, and the acquisition error rate is statistically calculated. After verification, the valid data is written into the permanent storage area and sorted according to the timestamps to form the operation status data. The data is organized through data structuring and divided into multiple file storage groups for subsequent processing and use.

[0071] The anomaly detection sub-module compares the current, voltage, and power ranges based on the operation status data, filters out the anomaly data, marks the frequent and sudden fluctuations, and categorically excludes the invalid anomaly points to obtain the anomaly data set.

[0072] By means of item-by-item inspection, each set of numerical values in the operating state data is compared with the preset normal range. The numerical values exceeding the range are marked as preliminary abnormal points. For each abnormal point, in combination with the data of multiple consecutive sampling points before and after, it is judged whether it belongs to sudden abnormality or frequent fluctuation. Sudden abnormality is judged by calculating the absolute change amount of adjacent point values, and frequent fluctuation is confirmed by statistically calculating the standard deviation of the change times and amplitudes of adjacent sampling points. Abnormal data caused by instantaneous sampling errors or short-term signal interference of the electric energy meter is excluded. For example, by identifying the duration of the abnormal point, isolated abnormal points within a single sampling period are excluded. For the marked frequent fluctuation data, clustering analysis is carried out, and the fluctuation phenomena with similar characteristics are grouped together, and low-amplitude fluctuations that contribute nothing to the analysis are excluded. Ineffective abnormal points are classified and marked and excluded from the abnormal data to ensure the accuracy of subsequent analysis. At the same time, the statistical information in the screening process is recorded to form an auxiliary statistical file of abnormal data, and finally an abnormal data set that has been classified and cleaned is obtained.

[0073] Based on the abnormal data set, the abnormal time series analysis sub-module sorts the abnormal data according to the time stamp, divides fixed time intervals, calculates the abnormal frequency of the intervals, marks the time periods with frequent abnormalities, extracts nodes and associates them with events, and establishes a record of parameter abnormal sequences;

[0074] Set fixed time intervals, for example, divide the intervals by minutes or hours, and count the abnormal data within each time interval. Calculate the occurrence frequency of the abnormal data. The abnormal frequency is obtained by accumulating the count values of the abnormal data in each interval. The intervals with higher frequency values are marked as key attention intervals. For the abnormal data in the intervals, further classify them according to the nature of the events, such as current abnormality, voltage abnormality, power abnormality, record the distribution of each type of abnormality in the intervals, extract the node with the highest occurrence frequency as an important parameter for time series analysis, and combine the time tags to associate the intervals with frequent abnormal occurrences to external events, such as peak electricity consumption or equipment maintenance periods, to form a preliminary association record of events and abnormalities. For the abnormal points that repeatedly appear in the time series, sequence clustering is carried out, and the abnormal phenomena with similar characteristics are divided into one class, and the characteristics of each class are extracted for classification and summary. A record of parameter abnormal sequences is established for all abnormal time series data, indicating the abnormal category, occurrence time, frequency and intensity, and arranging them by interval to provide complete basic data of abnormal time series for further in-depth analysis.

[0075] Specifically, as Figure 2 、 4 shown, the error spatio-temporal correlation modeling module includes:

[0076] Based on the record of parameter abnormal sequences, the node spatial relationship analysis sub-module extracts the node coordinate data, pairs and calculates the node distances, normalizes the distance values, marks the unmatched nodes as abnormal points, and generates a node distance matrix;

[0077] According to the spatial distribution rules of nodes, the coordinates of each node are divided into independent coordinate pairs and stored as computable structured data. The Euclidean distance between nodes is calculated pairwise. For all coordinate pairs of nodes, traversal is performed in a fixed pairing order, and the calculated distance values of each pair of nodes are recorded in a temporary matrix. After all node pairings are completed, the obtained distance data is normalized. Through the maximum-minimum normalization method, the distance values are scaled to the interval [0, 1]. The normalized values are stored in association with the original distance data. Unmatched nodes are screened by analyzing the node existence status in the parameter anomaly sequence record. For isolated nodes that do not appear in any node pairing, they are directly marked as abnormal points, and their abnormal status is recorded. The normalized distance values are integrated with the abnormal point annotation information to generate a node distance matrix, which contains the distance values between all nodes and the abnormal annotation status of unmatched nodes, providing input data support for subsequent analysis modules.

[0078] The spatial association grouping sub-module extracts adjacent relationship node pairs based on the node distance matrix, groups and labels nodes according to a distance threshold, maps the grouping information to the matrix, sorts and filters abnormal node pairs, and generates a spatial association annotation result;

[0079] According to the normalized values of the node distances, node pairs with a value less than the set distance threshold are selected as adjacent relationship node pairs, and their adjacent status is recorded. For each group of adjacent relationship node pairs, they are sorted according to the node numbers and distance values for subsequent grouping and labeling operations. The adjacent nodes are grouped according to the distance threshold. The identification information of each group of nodes is obtained by recursively searching the nodes within the same group, ensuring that all nodes within the same group have transitive connections. The grouping information is mapped to the node matrix, updating the grouping attribution information of each node. At the same time, a unique grouping identifier is assigned to each group of nodes. For the node pairs within the group, the node pairs that have been marked as abnormal are screened and filtered to ensure that only valid nodes are included in each group association. The grouping results are rechecked to eliminate isolated groups and invalid groups, generating a spatial association annotation result, which includes the node list of each group and the adjacent relationship data of each pair of nodes, while saving the annotation information of abnormal nodes.

[0080] The cross-node analysis sub-module analyzes the time series change trend based on the spatial association annotation result, combines the grouped data to analyze the node time association, marks abnormal association nodes, and generates an error spatio-temporal distribution matrix;

[0081] By performing linear interpolation and difference operations on the node time series data within each group, extracting the time correlation features between nodes, combining the grouped data analysis of node time correlations, statistically analyzing the similarity of the time change trends between each node and its adjacent nodes, marking the nodes with significantly different time change trends as abnormally associated nodes by calculating the correlation index of node time changes, analyzing the concentration degree of time changes of the nodes within the group according to the time series data distribution of each group of nodes, classifying and marking the nodes with significantly different change amplitudes from the average value as abnormally associated nodes, and combining the node time correlation features and the abnormal node marking status, an error spatio-temporal distribution matrix is generated. The matrix contains the time correlation status of the nodes, the error distribution characteristics, and the spatio-temporal abnormal distribution features, providing input support for further analysis of error propagation and spatio-temporal correlations.

[0082] Specifically, as Figure 2 、 5 shown, the error propagation path analysis module includes:

[0083] The weight value extraction sub-module, based on the error spatio-temporal distribution matrix, analyzes the numerical relationship of the nodes, extracts the transfer path weight values, matches and adjusts the weight value data, summarizes the node association relationships, and obtains the path transfer weight data;

[0084] Taking each node value in the matrix as the initial input of the weight value, using the time and space association relationships between nodes, analyzing the numerical transfer ratio between adjacent nodes one by one, extracting the weight value of each transfer path, calculating the transfer influence degree of each path by comparing the error distribution and time change trend of adjacent nodes, and adjusting the numerical range of the transfer weight value according to the distribution law to ensure that the weight value logically reflects the association strength between nodes. Matching the adjusted weight value data, recalibrating the weight values for the abnormal weight value points that appear within the same time period or the same spatial region, summarizing the node association relationships according to the weight adjustment results, extracting the participation weights of each group of nodes in the path, summarizing and organizing the complete path transfer weight data, and storing it in a structured manner, providing a basis for subsequent path weight value superposition analysis.

[0085] The path weight value superposition sub-module, based on the path transfer weight data, extracts the path node sequence, accumulates the node weight values, filters out the node combinations with high weights, sorts and screens the node paths, checks the integrity of the accumulation process, and generates a set of high-weight propagation paths;

[0086] Analyze the transfer order between path nodes, accumulate the weight values of each node on the path to form the total path weight, and record the intermediate calculation values during the accumulation process. By comparing the accumulation results of all paths, screen out the node combination with the highest weight value, and screen out the influence degree of key nodes in the path according to the weight ranking, record its specific position in the transfer path, further check the screened high-weight paths, check segment by segment whether the accumulation process of each path is complete, and ensure that there are no missing weight values or incorrect superpositions. Combine the screening results to finally generate a high-weight propagation path set, which includes the weight distribution and its accumulated value of all nodes in the path sequence.

[0087] Based on the high-weight propagation path set, the node propagation construction sub-module uses the dynamic time warping algorithm to analyze the node transfer sequence, extract the propagation direction, classify and reorganize the node transfer structure, sort out the continuous topological structure, and organize the transfer path of the nodes into a continuous topological structure to generate an error transfer path map.

[0088] The formula of the dynamic time warping algorithm is as follows:

[0089]

[0090] Among them, R ij represents the abnormal correlation score value between node i and node j, S i and S j represent the spatial correlation strength values of node i and node j respectively, T ij represents the difference value of the time series of node i and node j, C i and C j represent the classification feature values of node i and node j in the grouped data respectively. α is the weight coefficient of spatial correlation, β is the adjustment coefficient of time series change, and γ is the weight coefficient of grouping characteristics;

[0091] Parameter meaning and setting value:

[0092] S i and S j : The spatial correlation strength values of node i and node j, reflecting their weights in the spatial correlation annotation. Obtained by quantitatively analyzing the geographical location and spatial relationship of the nodes, set S i =0.8, S j =0.5;

[0093] T ij : The difference value of the time series of node i and node j, quantifying the deviation degree of the time change trends of the two nodes. Obtained by calculating the dynamic time warping (DTW) distance of the time series of the two nodes, set T ij =2.5;

[0094] C i and Cj : The classification feature values of node i and node j in the grouped data, which reflect the grouping characteristics of the nodes. Obtained by quantifying and encoding the attributes of the nodes, let C i = 1, C j = 2;

[0095] α: The weight coefficient of spatial association, used to adjust the influence intensity of spatial differences in the association score. Determined by the correlation analysis of historical data, let α = 1.2;

[0096] β: The adjustment coefficient of time series change, used to balance the contribution of time differences to the scoring results. Determined by the variance analysis of time series data, let β = 0.8;

[0097] γ: The weight coefficient of grouping characteristics, used to enhance the sensitivity of grouping characteristics to abnormal association detection. Determined by the dispersion degree of grouping characteristics, let γ = 0.5;

[0098] Substitute the parameters into the formula for calculation:

[0099] Calculate the absolute value of the difference in spatial association intensity: |S i - S j | = |0.8 - 0.5| = 0.3;

[0100] Calculate the absolute value of the difference in classification feature values: |C i - C j | = |1 - 2| = 1;

[0101] Calculate the weighted sum in the denominator: β·T ij + γ·|C i - C j | = 0.8·2.5 + 0.5·1 = 2 + 0.5 = 2.5;

[0102] Calculate the square root of the denominator:

[0103] Calculate the numerator: α·|S i - S j | = 1.2·0.3 = 0.36;

[0104] Calculate the abnormal association score value R ij :

[0105] The result R ij ≈0.228 indicates that there is a certain degree of abnormal association between node i and node j. This score value can be used for further analysis and marking of abnormally associated nodes to generate an error transmission path map.

[0106] Specifically, such as Figure 2 、6 As shown in

[0107] Based on the error transfer path map, the load distribution adjustment sub-module extracts the key node load ratio data, screens the nodes exceeding the threshold, re-divides the load data of the connection path, allocates and adjusts the load to adjacent nodes, and obtains the load adjustment distribution data;

[0108] Select the nodes with a higher load ratio from the map. By calculating the total load value and its proportion, screen out the nodes exceeding the set load threshold. For the nodes exceeding the threshold, analyze the load data in their connection paths, and re-divide the load ratio of the path according to the load distribution in the path, adjust the load distribution priority. During the load adjustment process, according to the remaining bearing capacity of adjacent nodes, transfer part of the load of the nodes exceeding the threshold to adjacent nodes, and record the path and transfer amount of the load transfer to ensure the balance after the load is re-allocated. After the adjustment is completed, re-statistics the load ratio of each node and path, obtains the load adjustment distribution data, and verifies the rationality and balance of the load adjustment.

[0109] Based on the load adjustment distribution data, the spare node screening sub-module analyzes the nodes with low load values, screens the nodes with remaining capacity as spare nodes, marks the paths as spare paths, updates the node-path association structure, and generates the spare node allocation result;

[0110] Screen the nodes whose load values are significantly lower than the average load level and have remaining bearing capacity, mark them as spare nodes, calculate the remaining capacity of the connection paths of each spare node, sort the nodes according to the size of the remaining capacity, and select the node with the largest remaining capacity as the preferred spare node. Combine the path load adjustment situation, mark the connection paths of the nodes meeting the spare conditions as spare paths. To ensure that the spare nodes and paths can meet the dynamic allocation requirements, update the association structure between nodes and paths, and generate the spare node allocation result, which includes the spare node list, spare path identification, and remaining capacity distribution.

[0111] Based on the spare node allocation result, the path load configuration sub-module extracts the spare node transfer path information, re-allocates the high-load path data, transfers the load to the spare path, updates the load value and verifies the structure, and generates the dynamic load allocation table;

[0112] Analyze the data in the high-load paths, select the paths where the load exceeds the adjusted target range for load redistribution, combine the remaining capacity of the standby nodes and the associated path information, transfer some of the load data on the high-load paths to the standby paths. The adjusted load data is allocated according to the path priority and standby capacity order. After the transfer is completed, update the load values of each node and path, and at the same time verify the adjusted structure, check the integrity and stability during the load distribution process, ensure that the load transfer does not introduce new overloaded nodes or disconnection situations, generate a dynamic load distribution table, which records the real-time load distribution of each path and the load change values of the nodes, provides a complete basis for dynamic load management, and provides support for load optimization and scheduling.

[0113] Specifically, as Figure 2 、 7 shown, the error adaptability verification module includes:

[0114] The node load monitoring sub-module, based on the dynamic load distribution table, analyzes the node load data, tracks the load change range, marks the out-of-range nodes, records the corresponding relationship between the load value and the time point, and obtains the dynamic node load data;

[0115] Extract the load values of the nodes at different time points, track the load change range of the nodes, mark the out-of-range nodes by comparing the node load values with the set normal load range, analyze the time period and frequency of the node load exceeding the range. For each out-of-range node, combine the time tags in the dynamic load distribution table to record the relationship between the overload value and the corresponding time point, form a time series of overloads. For the dynamic change process of the node load, count its change amplitude and change rate to identify the change pattern or sudden anomaly. After all node data analysis is completed, organize the dynamic load change situation into a node load change chart and form dynamic node load data to ensure that the data can clearly present the real-time load status and historical load change trajectory of the nodes, providing load input data for subsequent path stability verification.

[0116] The path stability verification sub-module, based on the dynamic node load data, analyzes and marks the node connection paths, checks the path load recovery time and stability, filters the continuously changing paths, and obtains the path response status data;

[0117] Analyze the out-of-range node connection paths of the tags, extract the load change conditions of the relevant paths. For each path, analyze the time series of load transfer between nodes. By statistically calculating the time length for the load to recover to the normal range in the path, judge the load recovery time of the path. Check the fluctuation range and frequency of the path load. By comparing the standard deviation and maximum value of the path fluctuations, confirm the load stability of the path. Screen out the paths with long-lasting load changes or significant fluctuation amplitudes, classify these paths as continuously changing paths, and record the status data of each path, including the start and end times of the fluctuations, the fluctuation amplitude, and the recovery time, etc. Organize the path status data into path response status data to indicate the dynamic load characteristics of each path.

[0118] Based on the path response status data, the node adaptability recording sub-module analyzes the key nodes of the abnormal paths, classifies and marks the overloaded nodes, calculates the adaptability-adjusted load value of the nodes, records the node load and delay characteristics, optimizes the node hierarchy structure, and generates the node adaptability verification results;

[0119] Calculate the adaptability-adjusted load value of the node according to the formula:

[0120]

[0121] where, L adj represents the adaptability-adjusted load value of the node, L i represents the current load value of the i-th node, μ represents the average value of the node loads, D i represents the delay time of the i-th node, μ d represents the average value of the node delays, C node represents the number of connections of the node, W 1 、W 2 、W 3 are weight parameters;

[0122] Detailed explanation of the formula and the derivation process of the formula calculation:

[0123] The formula is used to calculate the adaptability-adjusted load value of the node, and the obtained result is used to measure the comprehensive adaptability of the node load and delay, providing a quantitative basis for node hierarchy optimization;

[0124] L i represents the current load value of the i-th node, with the unit of requests per second. This value is obtained through real-time collection of the path response status data and is set to 120 (requests per second);

[0125] μ represents the average value of all node loads, with the unit of requests per second. The average value is calculated using all node load values in the path. Assuming that the node load values 100, 120, 140, 110, and 130 are obtained from the path data, the calculation shows

[0126] D i Represents the delay time of the i-th node, in milliseconds, which is obtained through the node response delay monitoring system and is set to 30 milliseconds;

[0127] μ d Represents the average value of all node delays, in milliseconds, calculated by taking the mean of the delay data of all nodes in the path. Assuming the delay time data is 20, 30, 40, 25, 35, the calculation results in

[0128] C node Represents the number of connections of the node, which is the number of other nodes directly connected to this node in the path. This value is obtained through path topology structure analysis and is set to 4;

[0129] W 1 、W 2 、W 3 Are weight parameters that respectively control the relative importance of load adjustment, the priority of delay optimization, and the regularization process of the denominator to ensure the stability and accuracy of the optimization process;

[0130] W 1 = 0.5, which is used to adjust the influence degree of the deviation between the load and the average value on the overall load adaptability. The weight setting refers to the sensitivity of the load change to the quality of service;

[0131] W 2 = 0.8, which is used to adjust the influence of the deviation between the delay and the average delay on the adaptability. The weight setting is based on the direct influence of the delay on the path efficiency;

[0132] W 3 = 2, which is used to regularize the influence of the denominator part. The setting basis is to avoid the instability of the calculation result caused by too small denominator;

[0133] Calculate the square root term of the load deviation:

[0134] Calculate the square root term of the delay deviation:

[0135] Calculate the denominator: W 3 + log(1 + C node ) = 2 + log(1 + 4) = 2 + log(5) ≈ 2 + 0.698 = 2.698;

[0136] Comprehensively calculate the node adaptability to adjust the load:

[0137] Result L adj ≈2.871 indicates that the adaptive adjustment load value of this node measures the comprehensive balance of its current load and latency. The higher the value, the closer the load and latency distribution of the node is to the overall level, and the better its adaptability. This result can be used as a reference for further optimizing the node hierarchy structure.

[0138] Specifically, as Figure 2 , 8 shown, the error transfer status summary module includes:

[0139] The error data archiving sub-module extracts node error information based on the node adaptability verification result, classifies and organizes the time points and node numbers, eliminates duplicate records, archives them in the order of time and nodes, and generates node error archive records;

[0140] Parse the error values one by one according to the timestamp and node number, and classify the error information into two categories: static error and dynamic error for classification and organization; for the time points in the record, rearrange them in the order of occurrence time to ensure that the error data can reflect the real time evolution process, and for the node numbers, perform uniqueness verification to eliminate redundant information caused by duplicate records or data merging, ensuring that each node number corresponds only to its unique error record. Screen the duplicate fluctuation information in the error records caused by error propagation or node transfer characteristics, eliminate invalid error fluctuation data. After deduplication and organization, archive all error information in the order of time points and nodes, and add an index to the archive records to ensure that the error information of a specific node or a specific time period can be quickly retrieved. Finally, generate node error archive records, providing a complete data structure including time series, node numbers, and error values.

[0141] The key error extraction sub-module extracts the error data between nodes based on the node error archive records, marks the out-of-range data points, organizes the node number and time series distribution, sorts the error values and marks the associated paths, generating a set of key error data points;

[0142] Analyze the error transfer characteristics between nodes, and screen the data according to the amplitude of the error values. Mark the data points that exceed the set error range. For the marked out-of-range data points, combine the distribution characteristics of the time series and node numbers, group the error points according to the node numbers, and reorganize them into a time series distribution table. Sort the error values of each group of nodes from largest to smallest in terms of error amplitude, and at the same time mark the associated transfer path for each data point to facilitate the analysis of the path characteristics in error transfer. After finishing the organization, screen the data points with larger error amplitudes and significant influence ranges, classify these data points as key error points, and form a set of key error data points. This data point set contains node numbers, time series distribution, error value sizes, and path annotation information.

[0143] Based on the key error data points set, the error monitoring summary sub-module analyzes the distribution law and node paths, records the error accumulation of nodes and paths, organizes the association relationships into a structured table, integrates the node status and error information, and generates an operation error monitoring report record form.

[0144] Analyze the distribution law of nodes and paths in the data points set, record the error accumulation value of each node and its propagation in the path one by one. Through cumulative calculation, analyze the total error accumulation of each path, and mark the paths with significant error accumulation as the key monitoring objects. Organize the error data of nodes and paths into a structured table, arranged respectively according to the time series, node numbers and path identifiers, so as to facilitate the comprehensive analysis of the spatial and temporal distribution of errors. Combining the results of node adaptability verification and the key error data points set, integrate the real-time status information and error information of nodes, including the overloaded status of nodes, the impact of error propagation and cumulative characteristics. Finally, generate an operation error monitoring report record form, which intuitively presents the error distribution, accumulation and key characteristics of nodes and paths, providing detailed data basis for subsequent optimization and improvement of the system.

[0145] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A smart electric energy meter capable of monitoring and reporting operating errors, characterized in that: The electric energy meter comprises: The error operation parameter monitoring module periodically collects the current value, voltage value and load power value of the electric energy meter, extracts abnormal states, sorts time and divides intervals, and establishes parameter abnormal sequence records; The error spatiotemporal correlation modeling module extracts the distance and adjacency of abnormal nodes based on the parameter abnormal sequence records, performs spatial grouping annotation and time frequency analysis, and generates an error spatiotemporal distribution matrix; The error propagation path analysis module extracts node weight values ​​and influencing factors based on the error spatiotemporal distribution matrix, accumulates node propagation relationships, screens high error paths, and constructs an error transmission path map; The error dynamic control module adjusts the load ratio and connection path based on the error transmission path map, selects spare nodes, allocates low-risk transmission paths, and generates a dynamic load distribution table; The error adaptability verification module collects load data, monitors the current and voltage variation range, verifies the path stability, records the response, and obtains the node adaptability verification result based on the dynamic load distribution table; The error transmission status summary module collects and archives error data based on the node adaptability verification results, analyzes node error changes, extracts key data points, and generates an operation error monitoring and reporting record table.

2. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 1 is characterized in that: The parameter anomaly sequence record includes an abnormal time period, an abnormal node mark, and an abnormal parameter value distribution. The error spatiotemporal distribution matrix includes node distance grouping, spatial correlation annotation, time series frequency analysis, and time series amplitude analysis. The error transmission path map includes node transmission weight, influencing factor distribution, propagation path direction, and path hierarchy. The dynamic load distribution table includes key node load ratio, spare node configuration, low-risk path allocation, and multi-path switching scheme. The node adaptability verification result includes key node anomaly mark, path response status, current and voltage change range monitoring results, and path connection stability verification. The operation error monitoring and reporting record table includes error data archiving records, node error change analysis, adaptability verification information extraction, and key data point statistics.

3. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 1 is characterized in that: The error operation parameter monitoring module includes: The data capture submodule periodically reads the current, voltage, and power data based on the working status of the electric energy meter, performs data acquisition at set time intervals, records signal loss and fluctuations, saves the acquired data after verification, and obtains the operating status data; The anomaly detection submodule compares the current, voltage, and power ranges based on the operating status data, filters abnormal data, marks frequent and sudden fluctuations, and classifies and removes invalid abnormal points to obtain an abnormal data set; The abnormal time series analysis submodule is based on the abnormal data set, sorts the abnormal data by timestamp, divides the fixed time interval, calculates the abnormal frequency of the interval, marks the frequent abnormal time period, extracts nodes and associates events, and establishes parameter abnormal sequence records.

4. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 1 is characterized in that: The error spatiotemporal correlation modeling module includes: The node spatial relationship analysis submodule extracts node coordinate data based on the parameter abnormal sequence record, calculates node distances by pairing, normalizes the distance values, marks unmatched nodes as abnormal points, and generates a node distance matrix; The spatial association grouping submodule extracts the node pairs with adjacent relationships based on the node distance matrix, groups and annotates the nodes according to the distance threshold, maps the grouping information to the matrix, sorts and filters the abnormal node pairs, and generates the spatial association annotation results; The cross-node analysis submodule analyzes the time series change trend based on the spatial association annotation results, analyzes the node time association in combination with the grouped data, marks abnormal associated nodes, and generates an error spatiotemporal distribution matrix.

5. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 1 is characterized in that: The error propagation path analysis module includes: The weight value extraction submodule analyzes the node value relationship based on the error spatiotemporal distribution matrix, extracts the transmission path weight value, matches and adjusts the weight value data, summarizes the node association relationship, and obtains the path transmission weight data; The path weight superposition submodule extracts the path node sequence based on the path transfer weight data, accumulates the node weight values, screens the node combination with high weight, sorts and screens the node path, checks the integrity of the accumulation process, and generates a high-weight propagation path set; The node propagation construction submodule is based on the high-weight propagation path set and adopts a dynamic time warping algorithm to parse the node transmission sequence, extract the propagation direction, classify and reorganize the node transmission structure, organize the continuous topological structure, and organize the node transmission path into a continuous topological structure to generate an error transmission path map.

6. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 5 is characterized in that: The formula of the dynamic time warping algorithm is as follows: Among them, R ij represents the abnormal correlation score between node i and node j, S i and S j Represent the spatial correlation strength values ​​of node i and node j respectively, T ij represents the difference between the time series of node i and node j, C i and C j They represent the classification feature values ​​of node i and node j in the grouped data respectively, α is the weight coefficient of spatial association, β is the adjustment coefficient of time series change, and γ is the grouping feature weight coefficient.

7. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 1 is characterized in that: The error dynamic control module includes: The load distribution adjustment submodule extracts the load proportion data of key nodes based on the error transmission path map, screens the nodes exceeding the threshold value, re-divides the connection path load data, distributes and adjusts the load to adjacent nodes, and obtains the load adjustment distribution data; The standby node screening submodule parses nodes with low load values ​​based on the load adjustment distribution data, screens nodes with remaining capacity as standby nodes, marks paths as standby paths, updates the node and path association structure, and generates standby node allocation results; The path load configuration submodule extracts the backup node transfer path information based on the backup node allocation result, reallocates the high-load path data, transfers the load to the backup path, updates the load value and verifies the structure, and generates a dynamic load distribution table.

8. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 1 is characterized in that: The error adaptability verification module includes: The node load monitoring submodule parses the node load data based on the dynamic load distribution table, tracks the load change range, marks the out-of-range nodes, records the corresponding relationship between the load value and the time point, and obtains the dynamic node load data; The path stability verification submodule parses the marked node connection path based on the dynamic node load data, checks the path load recovery time and stability, screens the continuously changing path, and obtains the path response status data; The node adaptability recording submodule analyzes the key nodes of the abnormal path based on the path response status data, classifies and marks the overloaded nodes, calculates the adaptive adjustment load value of the node, records the node load and delay characteristics, optimizes the node hierarchy structure, and generates the node adaptability verification result.

9. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 8, characterized in that: Calculate the adaptive load value of the node according to the formula: Among them, L adj Represents the adaptive load value of the node, L i represents the current load value of the i-th node, μ represents the average value of the node load, and D i represents the delay time of the i-th node, μ d represents the average value of node delay, C node Represents the number of connections of the node, and W1, W2, and W3 are weight parameters.

10. The smart electric energy meter capable of monitoring and reporting operating errors according to claim 1, characterized in that: The error transfer state summary module includes: The error data archiving submodule extracts node error information based on the node adaptability verification result, classifies and sorts time points and node numbers, removes duplicate records, archives them in time and node order, and generates node error archiving records; The key error extraction submodule extracts the error data between nodes based on the node error archive records, marks the out-of-range data points, sorts the node numbers and time series distribution, sorts the error values ​​and marks the associated paths, and generates a key error data point set; The error monitoring summary submodule analyzes the distribution rules and node paths based on the key error data point set, records the accumulated node and path errors, organizes the associations into a structured table, integrates the node status and error information, and generates an operation error monitoring reporting record table.

Images